Detection and Analysis of Asphalt Pavement Texture Depth Based on Digital Image Analytics Technology

被引:8
作者
Yu, Dezhong [1 ]
Cao, Yang [1 ]
Zhao, Qianqian [2 ]
机构
[1] Zhejiang Shuren Univ, Dept Urban Construct, Hangzhou 310015, Zhejiang, Peoples R China
[2] Northeast Agr Univ, Dept Water Conservancy & Civil Engn, Harbin 150080, Heilongjiang, Peoples R China
关键词
Pavement engineering; Asphalt mixture; Gradation segregation; Texture depth; Image analytics; SURFACE TEXTURE; PARAMETERS;
D O I
10.1007/s42947-023-00368-x
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Texture depth is a main index of quality control of asphalt pavement construction, the gradation deviation of mixture under various factors will also lead to the change of pavement texture depth. To analyze the applicability of texture depth to reflect the segregation of asphalt pavement gradation, AC-25 ordinary asphalt mixture, AC-20 rubber asphalt mixture, and AC-16 rubber asphalt mixture were selected as the research objects, and high-definition images of each structural layer were collected. An image processing program was developed using MATLAB software, the gray difference was used to characterize the height difference between the convex and concave of the road surface, and the image was processed by wavelet denoising, the relationship model between the construction depth of asphalt pavement sanding method and the construction depth of digital image was established. The test results show the correlation coefficient R2 = 0.8745 between the texture depth data detected by image analytics technology and the data detected by on-site sand patch method indicates that the two have a good linear relationship. There is a strong positive linear relationship between the deviation degree of mixture gradation and the deviation degree of texture depth; texture depth index can effectively reflect the deviation of asphalt mixture gradation.
引用
收藏
页码:628 / 637
页数:10
相关论文
共 25 条
[1]   Investigating the Micro/Macro-Texture Performance of Roller-Compacted Concrete Pavement under Simulated Traffic Abrasion [J].
Adresi, Mostafa ;
Lacidogna, Giuseppe .
APPLIED SCIENCES-BASEL, 2021, 11 (12)
[2]   Prediction modeling of skid resistance and texture depth on flexible pavement for urban roads [J].
Athiappan, K. ;
Kandasamy, A. ;
Mohamed, M. Jinnah Sheik ;
Parthiban, P. ;
Balasubramanian, S. .
MATERIALS TODAY-PROCEEDINGS, 2022, 52 :923-929
[3]   Clogging potential evaluation of porous mixture surfaces used in permeable pavement systems [J].
Brugin, Matteo ;
Marchioni, Mariana ;
Becciu, Gianfranco ;
Giustozzi, Filippo ;
Toraldo, Emanuele ;
Andres-Valeri, Valerio Carlos .
EUROPEAN JOURNAL OF ENVIRONMENTAL AND CIVIL ENGINEERING, 2020, 24 (05) :620-630
[4]   A state-of-the-art review of asphalt pavement surface texture and its measurement techniques [J].
Chen, Siyu ;
Liu, Xiyin ;
Luo, Haoyuan ;
Yu, Jiangmiao ;
Chen, Fuda ;
Zhang, Yang ;
Ma, Tao ;
Huang, Xiaoming .
JOURNAL OF ROAD ENGINEERING, 2022, 2 (02) :156-180
[5]   Influence of effective texture depth on pavement friction based on 3D texture area [J].
Ding, Shihai ;
Wang, Kelvin C. P. ;
Yang, Enhui ;
Zhan, You .
CONSTRUCTION AND BUILDING MATERIALS, 2021, 287 (287)
[6]   Asphalt pavement macrotexture reconstruction from monocular image based on deep convolutional neural network [J].
Dong, Shihao ;
Han, Sen ;
Wu, Chi ;
Xu, Ouming ;
Kong, Haiyu .
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2022, 37 (13) :1754-1768
[7]   Correction of texture depth of porous asphalt pavement based on CT scanning technique [J].
Gao, Lei ;
Liu, Mingxi ;
Wang, Zhanqi ;
Xie, Jianguang ;
Jia, Sicheng .
CONSTRUCTION AND BUILDING MATERIALS, 2019, 200 :514-520
[8]   Cellular-phone-based computer vision system to extract shape properties of coarse aggregate for asphalt mixtures [J].
Ghuzlan, Khalid A. ;
Obaidat, Mohammed T. ;
Alawneh, Mai M. .
ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH, 2019, 22 (03) :767-776
[9]   Evaluation and Comparison of Real-Time Laser and Electric Sand-Patch Pavement Texture-Depth Measurement Methods [J].
Hao, Xueli ;
Sha, Aimin ;
Sun, Zhaoyun ;
Li, Wei ;
Zhao, Haiwei .
JOURNAL OF TRANSPORTATION ENGINEERING, 2016, 142 (07)
[10]  
Huang Z., 2017, J HEFEI U TECHNOLOGY, V40, P1382